Object tracking has become increasingly prevalent in modern applications. This is particularly true in the field of vehicle tracking. Therefore, it is increasingly necessary for optimization of tracking algorithms and corresponding parameter settings.
For example, a rule specifying that a vehicle cannot climb a wall could be beneficial in developing a tracking algorithm. In practice, a common solution is to have a human manually specify suitable regions for object detection and tracking, and ignoring other regions such as walls. However, human intervention in such algorithms is expensive, time-consuming, and error prone. Therefore, it would be beneficial to automate the process of setting parameters for tracking algorithms utilizing application-dependent and environment-dependent information.